NumPy shape 与 reshape 区别
一. 基本使用
In [438]: x = np.arange(12)
In [439]: y = x # same object
In [440]: y.shape = (2,6) # inplace shape change
In [441]: y
Out[441]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [442]: x
Out[442]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [443]: y = y.reshape(3,4) # y is a new view
In [444]: y
Out[444]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [445]: x # buffer data is not change
Out[445]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
二. 具体介绍
可以使用 numpy array 的 shape 属性值, 或者 reshape 方法来修改 numpy 对象的 shape.
- 直接
shape赋值, 修改了 numpy array 的 data buffer - 使用
reshape方法, 仅仅返回了一个视图
三. Data Buffer 与 View
numpy array 变量有一个对应的内存实体 (data buffers), 同时可以有多个视图 (view).
- shape 直接改变 data buffer; reshape 则是返回一个新的 view.
- 不同索引/切片 (index) 方法对应于不同的返回结果 (view 或者是 copy)
待总结
Arrays can also have views, which are new array objects, but with shared data buffers.
A copy has its own data buffer.
